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       "'\\n    @Author: King\\n    @Date: 2019.06.25\\n    @Purpose: Graph Convolutional Network\\n    @Introduction:   \\n    @Datasets: \\n    @Link : \\n    @Reference : https://docs.dgl.ai/tutorials/models/1_gnn/1_gcn.html\\n'"
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   "source": [
    "'''\n",
    "    @Author: King\n",
    "    @Date: 2019.06.25\n",
    "    @Purpose: Graph Convolutional Network\n",
    "    @Introduction:   This is a gentle introduction of using DGL to implement \n",
    "                    Graph Convolutional Networks (Kipf & Welling et al., \n",
    "                    Semi-Supervised Classification with Graph Convolutional Networks). \n",
    "                    We build upon the earlier tutorial on DGLGraph and demonstrate how DGL \n",
    "                    combines graph with deep neural network and learn structural representations.\n",
    "    @Datasets: \n",
    "    @Link : \n",
    "    @Reference : https://docs.dgl.ai/tutorials/models/1_gnn/1_gcn.html\n",
    "'''"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Graph Convolutional Network\n",
    "\n",
    "## Model Overview\n",
    "\n",
    "### GCN from the perspective of message passing\n",
    "\n",
    "We describe a layer of graph convolutional neural network from a message passing perspective; the math can be found here(https://docs.dgl.ai/tutorials/models/1_gnn/1_gcn.html#math). It boils down to the following step, for each node u:\n",
    "\n",
    "\n",
    "1) Aggregate neighbors’ representations $h_{v}$ to produce an intermediate representation $\\hat{h}_{u}$.\n",
    "2) Transform the aggregated representation $\\hat{h}_{u}$ with a linear projection followed by a non-linearity: $h_{u}=f\\left(W_{u} \\hat{h}_{u}\\right)$.\n",
    "\n"
   ]
  }
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